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- .claude/CLAUDE.md - .claude/commands/subagentes.md - .claude/rules/INDEX.md - .mcp.json - bash/functions/cybersecurity/analyze_dns.md - bash/functions/cybersecurity/audit_http_headers.md - bash/functions/cybersecurity/audit_ssh_config.md - bash/functions/cybersecurity/check_firewall.md - bash/functions/cybersecurity/detect_suspicious_users.md - bash/functions/cybersecurity/encrypt_file.md - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
49 lines
1.6 KiB
Markdown
49 lines
1.6 KiB
Markdown
---
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name: embedding_encode
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kind: function
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lang: py
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domain: infra
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version: "1.0.0"
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purity: impure
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signature: "def embedding_encode(model: SentenceTransformer, texts: list, mode: str = 'document') -> list"
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description: "Genera embeddings normalizados para textos. Aplica prefijos e5 automaticamente segun mode (document/query)."
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tags: [embedding, encode, e5, multilingual, python, pendiente-usar]
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uses_functions: [embedding_load_model_py_infra]
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uses_types: []
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returns: []
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returns_optional: false
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error_type: "error_go_core"
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imports: [sentence_transformers]
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params:
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- name: model
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desc: "instancia SentenceTransformer cargada con embedding_load_model"
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- name: texts
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desc: "lista de strings a codificar como embeddings"
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- name: mode
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desc: "contexto semántico: 'document' para indexación, 'query' para búsqueda (aplica prefijos e5)"
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output: "list[list[float]]: embeddings normalizados (L2=1), dimensión 384 para e5-small"
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tested: false
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tests: []
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test_file_path: ""
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file_path: "python/functions/embedding/model.py"
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---
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## Ejemplo
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```python
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model = embedding_load_model(".local/models/e5-small")
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# Indexar documentos
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doc_embs = embedding_encode(model, ["La IA transforma la industria", "Python es versatil"], mode="document")
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# Buscar
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query_embs = embedding_encode(model, ["¿Que es machine learning?"], mode="query")
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```
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## Notas
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mode="document" agrega prefijo "passage: ", mode="query" agrega "query: ".
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Estos prefijos son requeridos por modelos e5 para retrieval optimo.
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Los embeddings retornados son float32 normalizados (norma L2 = 1).
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Para e5-small la dimension es 384. Throughput ~1900 docs/s en CPU.
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